Staff Data Reliability Engineer

The Hartford

Confirmed live yesterday High trust
Hybrid

Quick summary

Work type
Hybrid
Location
Hartford, CTChicago, ILColumbus, OHCharlotte, NC
Salary
$127,600–$191,400 / yr
Posted
22 days ago
Freshness
Confirmed live yesterday

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $189k
This role $160k
$115k most similar roles pay here $242k

This role pays less than 75% of similar roles. Most pay $160,778–$218,056 — the shaded band above. At the midpoint, this role pays about $160k versus about $189k for comparable roles.

Based on 240 similar postings.

Employer

About The Hartford

The Hartford is a leading provider of property and casualty insurance, group benefits, and mutual funds, serving businesses and individuals across the United States. Industry: Insurance & Financial Services

The Hartford currently has 45 open roles on FindRole.

Listed pay typically runs $127,600–$191,400 across 37 roles with salary data.

Most-posted roles

View all roles at The Hartford

At a glance

TL;DR · Staff Data Reliability Engineer

Staff Data Reliability Engineer - Hybrid joins the SRE team to focus on the integrity, quality, and availability of data assets and pipelines. This role applies SRE principles to the data journey layer, ensuring products are trustworthy for downstream consumers. You will establish Data Service Level Objectives for freshness and accuracy, implement advanced observability tools to detect schema drifts or delays, and automate manual tasks like data backfilling and reprocessing to eliminate toil. The role involves building automated runbooks and managing data-specific incidents. Key technologies include Informatica, Python, PySpark, Amazon EMR, Hadoop, Snowflake, AWS, GCP, SSIS, and SSMS. You will also utilize AIOps for anomaly detection, prompt engineering with AI services, and DataOps practices to manage the lifecycle of data in motion and at rest within an enterprise environment.

What you'll do

  • Establish and enforce Data Service Level Objectives (SLOs) for data freshness, completeness, and accuracy.
  • Implement advanced observability tools to monitor the entire data journey and detect anomalies or schema drifts.
  • Embed reliability patterns into data pipelines using Informatica, Python/PySpark, and Amazon EMR/Hadoop.
  • Automate manual operational tasks such as data validation, reprocessing, and backfilling to reduce toil.
  • Lead the response and resolution for data-related incidents including corrupt data or delayed reporting.
  • Develop and automate sophisticated, data-aware runbooks for common pipeline failures and recovery scenarios.
  • Implement AIOps and machine learning principles for automated anomaly detection and self-healing pipelines.

What we're looking for

  • Bachelor's degree and 5+ years of experience in an Infrastructure, Data, or related technology organization as a hands-on technologist.
  • 3+ years of experience in Data Engineering, Data Quality, or a specialized SRE role within an enterprise data environment.
  • Hands-on experience with Snowflake, AWS/GCP cloud environments, and ETL pipelines using SSIS and SSMS.
  • Hands-on experience with Informatica, Python/Pyspark, and distributed compute (EMR/Hadoop).
  • Experience in designing data quality checks, validation frameworks, governance standards, and DataOps practices.
  • Expertise in data observability tools, AIOps for pipeline self-healing, and AI-enabled automation for data reliability.
  • Experience with .NET framework (C# or VB.NET) and ASP.NET web applications (preferred).

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